Structural Robustness for Deep Learning Architectures

This success can be just ified based on their universal approximation properties [6], whi ch allow them to approximate any function that associates each train ing set input to its corresponding class. But this is also a double-edg ed sword, as the resulting function may not handle well domain shifts ( i.e., it does not generalize well to previously unseen inputs). Adve rsarial attacks (i.e., imperceptible changes to the input built spe cifically to fool the network function) [7, 8] illustrate the risks of bad generalization. Isotropic noise [9] or corrupted inputs [10] are al so likely to produce similar misclassifications. In applications tha t are very sensitive to errors, such as autonomous vehicles or robotic assisted surgery, robustness to such deviations is a key challenge.